Results 21 to 30 of about 18,495 (249)
Improved Local Search with Momentum for Bayesian Networks Structure Learning
Entropy, 2021 Bayesian Networks structure learning (BNSL) is a troublesome problem that aims to search for an optimal structure. An exact search tends to sacrifice a significant amount of time and memory to promote accuracy, while the local search can tackle complex ...
Xiaohan Liu +3 more
doaj +1 more source
Frontiers in Computer Science, 2020 Bayesian probabilistic modeling is supported by powerful computational tools like probabilistic programming and efficient Markov Chain Monte Carlo (MCMC) sampling. However, the results of Bayesian inference are challenging for users to interpret in tasks
Evdoxia Taka +2 more
doaj +1 more source
Entropy, 2020 Score-based algorithms that learn Bayesian Network (BN) structures provide solutions ranging from different levels of approximate learning to exact learning.
Zhigao Guo, Anthony C. Constantinou
doaj +1 more source
Computation of Kullback–Leibler Divergence in Bayesian Networks
Entropy, 2021 Kullback–Leibler divergence KL(p,q) is the standard measure of error when we have a true probability distribution p which is approximate with probability distribution q.
Serafín Moral +2 more
doaj +1 more source
Probabilistic knowledge-based characterization of conceptual geological models
Applied Computing and Geosciences, 2021 The construction of conceptual geological models is an essential task in petroleum exploration, especially during the early stages of investment, when evidence about the subsurface is limited.
Júlio Hoffimann +11 more
doaj +1 more source
Bayesian Test of Significance for Conditional Independence: The Multinomial Model
Entropy, 2014 Conditional independence tests have received special attention lately in machine learning and computational intelligence related literature as an important indicator of the relationship among the variables used by their models.
Pablo de Morais Andrade +2 more
doaj +1 more source
Inference Attacks on Genomic Data Based on Probabilistic Graphical Models
Big Data Mining and Analytics, 2020 The rapid progress and plummeting costs of human-genome sequencing enable the availability of large amount of personal biomedical information, leading to one of the most important concerns — genomic data privacy. Since personal biomedical data are highly
Zaobo He, Junxiu Zhou
doaj +1 more source
SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting
Forecasting, 2022 Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR model, which incorporates machine learning (ML) into the epidemiological ...
Roberto Vega +2 more
doaj +1 more source
Deep Probabilistic Graphical Modeling
CoRR, 2020 Probabilistic graphical modeling (PGM) provides a framework for formulating an interpretable generative process of data and expressing uncertainty about unknowns, but it lacks flexibility. Deep learning (DL) is an alternative framework for learning from data that has achieved great empirical success in recent years.
openaire +3 more sources
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
Physical Review X, 2017 Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions.
Marcello Benedetti +3 more
doaj +1 more source